Digitalisation, Major Projects & Infrastructure

Getting digital engineering right: DCWC combining technology with expertise

digital engineering

With the economy depending upon the current infrastructure pipeline to lift the country out of recession, it is imperative that projects reliant upon digital engineering are underpinned by human expertise.

When it comes to risk management and quantity surveying, grey hair is an asset, says Peter Gill, managing director – Infrastructure at Donald Cant Watts Corke (DCWC).

“You’ll never get rid of that human factor, the grey hair around the table,” said Gill. “We need those people in the room to give certainty.”

To Gill, this need for wisdom and experience is only made more prevalent by the adoption of digital engineering models and their increasing popularity as tools for estimating costs in the early stages of major infrastructure projects. Under pressure to prepare projects to be put to market or provide a cost figure, digital engineering models are being used that are missing important information, leading to potholes in the delivery stages.

“My current opinion is that there isn’t enough being invested into digital engineering or building information modelling (BIM),” said Gill. “If you don’t invest enough capital and time into digital engineering, you end up with clashes in the various levels of the drawing, missing detail and you can’t link up the quantities to one another.”

In cases where digital engineering is done right, the results can be astounding. However, these are built off the back of solid work in the production of the models. Gill notes that work done on the digital model of the Sydney Opera House could be the benchmark for projects in Australia.

“The Sydney Opera House has invested in their BIM and they have the perfect digital engineering model, from 2.5m below sea level to 40m above sea level. They’ve got a truly 6D, digital engineering BIM model which links individual three-dimensional project data to time scheduling, cost, and the project lifecycle. That’s the kind of investment that you need to put into each project, and if you don’t invest time and effort, then there’s going to be a huge amount of data missing.”

Notwithstanding the Opera House’s own complexities, for infrastructure projects that stretch over a larger distance there are added difficulties in constructing a digital model, however this does not preclude proper processes.

“Taking shortcuts and trying to get historic data from a range of infrastructure projects and put it into a database for a digital engineering model for costing a new project is dangerous if you don’t invest the correct time into each project,” said Gill.

“It takes time, cost, and effort, and requires enabling the engineers to do the job properly. Even then, you’ll only get 70 per cent of the information you require for the model. You still need that human factor, subject matter experts, to come and make the difference.”

At the end of the day, a digital model is only as good as the data that is fed into it, said Gill.

“If it’s rubbish in, it’s rubbish out. If you don’t put in the time and effort and the detail into that model, then the confidence and the assurance of the outcomes or the certainty of the outcomes is not going to be as good.”

PUTTING THE PIECES BACK TOGETHER

One doesn’t have to look far to see some of the mistakes that have occurred when models have been used without sufficient data. Gill highlights the Sydney Light Rail as one example. A lack of data on underground utilities in the CBD led to the project spiralling well above budget and over time.

“There wasn’t enough time taken to do the technical investigations, the dial before you dig investigations on Sydney Light Rail,” said Gill. “If you don’t spend the time, you need people who know the area to come to the meetings and actually provide that information. Either way you’ve got to pay for the cost upfront before the project starts or it’s going to cost you a huge amount of money later, never mind the reputational issues associated with it.”

On other projects, as scope and requirements have changed as the project developed, models needed to be upgraded to reflect these changes.

“We’ve been involved as a company measuring and pricing those components using traditional methods because the design is changing so fast,” said Gill. “BIM models are great when the project is finished and you have all the as-built information, this can then be used in the operations phase, but during the development phase, you still need those tried and trusted methods, the 2D measurement checking methods to make sure and provide certainty on the accuracy of the BIM model outputs.”

As with any model, there is only so much that can be included. No model will ever be a 1:1 representation of a project with information on all impacting factors included. To bridge this gap, expertise is needed to know where the risks are.

“What you need to do with digital modelling is you need to pick out the information that you’ve got, put it on a table and have subject matter experts have a discussion about what’s missing,” said Gill. “You have to invest that time and effort to look at every single thing that’s missing and then put an estimate to it so that the government has the correct price before the project even starts.”

Gill is adamant that this process needs to be followed, and that digital tools must be supplemented with first-hand knowledge.

“On every single infrastructure project, we need to implement that process. Even if the contractors have a BIM model in place, it doesn’t require a lot of money to conduct a two to three-week exercise where you take some of the drawings and measure them. We measure and bulk check quantities and information on 2D drawings and compare it to what the BIM models are actually producing. If we do enough samples over this period, we can actually let them know how accurate the BIM models are as a percentage, and then we know how much effort and time we have to put into make up the difference.”

These procedures do not obviate the need for digital engineering, but instead allow the best decisions to be made based on the model with an understanding of what is not present, and the risks involved in that.

“The earlier you bring in the subject matter experts to look at the uncertainty – from quantities rates and construction methodology, to the site specific risks such as latent conditions, underground services, labour issues, approval issues, stakeholder management – the better the certainty is in the project outcomes. It’s purely a matter of having the right people around the table to provide the right information – people who know and understand the area,” said Gill. “It’s that simple, you’ve got to have the human factor in the process.”

Gill said that at DCWC, cost and quantity surveying tasks are done with the benefit of in-house expertise and outside experts where needed.

“DCWC has nine divisions and advisors, we have project managers, quantity surveyors, and we have infrastructure experts in the field. Where we fall short, we bring in experts in the field so, we’ll partner with organisations and individuals who have tunnelling experience, those who have actually worked at the coalface. We will find and bring in those experts to make sure that the government gets the right advice.”

Once this is done, completed work is reviewed for accuracy.

“You need the right people to challenge thinking and verify the information, then the final step is the peer review,” said Gill. “Every project that we do will have somebody doing a peer review to make sure that it’s correct.”

From there, the beauty of a digital model can be realised.

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